We present a new data-driven approach with physics-based priors to
scene-level normal estimation from a single polarization image. Existing shape
from polarization (SfP) works mainly focus on estimating the normal of a single
object rather than complex scenes in the wild. A key barrier to high-quality
scene-level SfP is the lack of real-world SfP data in complex scenes. Hence, we
contribute the first real-world scene-level SfP dataset with paired input
polarization images and ground-truth normal maps. Then we propose a
learning-based framework with a multi-head self-attention module and viewing
encoding, which is designed to handle increasing polarization ambiguities
caused by complex materials and non-orthographic projection in scene-level SfP.
Our trained model can be generalized to far-field outdoor scenes as the
relationship between polarized light and surface normals is not affected by
distance. Experimental results demonstrate that our approach significantly
outperforms existing SfP models on two datasets. Our dataset and source code
will be publicly available at this https URL

该研究提出了一种新的基于数据驱动和物理学先验信息的场景级别单极化图像法线估计方法，并且建立了第一个真实场景级别形态恢复数据集。该方法采用了多头自注意力与视图编码来处理在野外环境中形成的极化杂波和联机投影问题，实验结果表明，相对于现有的形态恢复方法，该方法具有更好的性能。